library(Seurat)
library(ggplot2)
library(pheatmap)
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
✓ tibble  3.1.3     ✓ stringr 1.4.0
✓ readr   2.0.1     ✓ forcats 0.5.1
✓ purrr   0.3.4     
── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()
fig_dir = file.path("figs","brain_plot")
dir.create(fig_dir)
Warning in dir.create(fig_dir) : 'figs/brain_plot' already exists
srt95 = saveRDS(file = "data/srt_E95.Rds")
Error in saveRDS(file = "data/srt_E95.Rds") : 
  argument "object" is missing, with no default
srt33 = srt95[,srt95$orig.ident=="201104_33"]
library(dbscan)


db_out = dbscan(srt33@reductions$spatial@cell.embeddings,60)

ggplot(data=NULL,aes(x=srt33@reductions$spatial@cell.embeddings[,1],y=srt33@reductions$spatial@cell.embeddings[,2],col= factor(db_out$cluster)))+
         geom_point(size=0.8)+
  coord_fixed()+
  theme_classic()


sel_clu = names(table(db_out$cluster)[order(table(db_out$cluster),decreasing = T)][1])
sel_clu
[1] "1"
srt33 = srt33[,db_out$cluster==sel_clu]

saveRDS(srt33,file="data/srt33_clean.Rds")
sel_cluster = 23
DimPlot(srt33,reduction = "spatial",cells.highlight=colnames(srt33)[srt33$seurat_clusters==sel_cluster])+ggtitle(paste0("cluster ",sel_cluster))+coord_fixed()

srt_sel = srt33[,srt33$seurat_clusters==sel_cluster]

db_out = dbscan(srt_sel@reductions$spatial@cell.embeddings,60)

ggplot(data=NULL,aes(x=srt_sel@reductions$spatial@cell.embeddings[,1],y=srt_sel@reductions$spatial@cell.embeddings[,2],col= factor(db_out$cluster)))+
         geom_point(size=0.8)+
  coord_fixed()+
  theme_classic()


sel_clu = names(table(db_out$cluster)[order(table(db_out$cluster),decreasing = T)][1])
sel_clu
[1] "1"
srt_sel = srt_sel[,db_out$cluster==sel_clu]
library(edgeR)
Loading required package: limma
library(limma)

cnts = srt_sel@assays$RNA@counts
cnts = cnts[rowSums(cnts)>20,]
cnts = as.matrix(cnts)
allcounts = DGEList(counts=cnts)
allcounts = estimateDisp(allcounts, robust=TRUE)
Using classic mode.
  design_mat = model.matrix(~ srt_sel@reductions$spatial@cell.embeddings[,2])
  fit = glmQLFit(allcounts, design_mat)
  lrt = glmQLFTest(fit)
  top = topTags(lrt,n=Inf)
  top
Coefficient:  srt_sel@reductions$spatial@cell.embeddings[, 2] 
srt_markers = srt_sel@misc$markers
top10 <- srt_markers[srt_markers$p_val_adj<0.01,] #%>% group_by(cluster) %>% top_n(n = 30, wt = -p_val_adj) # %>%  top_n(n = 5, wt = avg_logFC)
top10$pct_diff = top10$pct.1-top10$pct.2
top10 = top10[top10$pct_diff>0.1,]
top10 = top10 %>% group_by(gene) %>% top_n(n=1,wt=avg_log2FC)

top10 = top10[top10$cluster %in% sel_cluster,]
top_sel = top$table[rownames(top$table) %in% top10$gene,]
top_sel = top_sel[top_sel$FDR<0.05,]
write.csv(top_sel,file =file.path(fig_dir, paste0("DE_markersforcluster_",sel_cluster,".csv")))
top_sel
sel_gene = c("Tcf7l2","Nr2f2","Wfdc1","Tal2","Boc","Zic4")
FeaturePlot(srt_sel,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,pt.size=0.5,ncol=3)
ggsave(file.path(fig_dir,paste0("markergene_",sel_cluster,"_region.pdf")))
Saving 7.29 x 4.5 in image

FeaturePlot(srt33,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)

ggsave(file.path(fig_dir,paste0("markergene_",sel_cluster,"_allpuck.pdf")))
Saving 9 x 4 in image
sel_clusters = c(23, 27)
srt_sel = srt33[,srt33$seurat_clusters %in% sel_clusters]

db_out = dbscan(srt_sel@reductions$spatial@cell.embeddings,60)

ggplot(data=NULL,aes(x=srt_sel@reductions$spatial@cell.embeddings[,1],y=srt_sel@reductions$spatial@cell.embeddings[,2],col= factor(db_out$cluster)))+
         geom_point(size=0.8)+
  coord_fixed()+
  theme_classic()


sel_clu = names(table(db_out$cluster)[order(table(db_out$cluster),decreasing = T)][1])

srt_sel = srt_sel[,db_out$cluster==sel_clu]


cnts = srt_sel@assays$RNA@counts
cnts = cnts[rowSums(cnts)>20,]
cnts = as.matrix(cnts)
allcounts = DGEList(counts=cnts)
allcounts = estimateDisp(allcounts, robust=TRUE)
Using classic mode.
  design_mat = model.matrix(~ srt_sel@reductions$spatial@cell.embeddings[,2])
  fit = glmQLFit(allcounts, design_mat)
  lrt = glmQLFTest(fit)
  top = topTags(lrt,n=Inf)


srt_markers = srt_sel@misc$markers
top10 <- srt_markers[srt_markers$p_val_adj<0.01,] #%>% group_by(cluster) %>% top_n(n = 30, wt = -p_val_adj) # %>%  top_n(n = 5, wt = avg_logFC)
top10$pct_diff = top10$pct.1-top10$pct.2
top10 = top10[top10$pct_diff>0.2,]
#top10 = top10 %>% group_by(gene) %>% top_n(n=1,wt=avg_log2FC)

top10 = top10[top10$cluster %in% sel_clusters,]

top_sel = top$table[rownames(top$table) %in% top10$gene,]
top_sel = top_sel[top_sel$FDR<0.05,]
write.csv(top_sel,file =file.path(fig_dir, paste0("DE_markersforcluster_23_27.csv")))
top_sel
sel_gene = c("Tcf7l2","Dmrta2","Rprm","Tubb3","Pcdh8","Shh")
FeaturePlot(srt_sel,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,pt.size=0.5,ncol=3)
ggsave(file.path(fig_dir,paste0("markergene_23_27_region.pdf")))
Saving 7.29 x 4.5 in image

FeaturePlot(srt33,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)

ggsave(file.path(fig_dir,paste0("markergene_23_27_allpuck.pdf")))
Saving 9 x 4 in image

srt_sel$MHB = "no"
srt_sel$MHB[srt_sel$ycoord>2900 & srt_sel$ycoord < 3180 & srt_sel$xcoord>4900] = "yes"

DimPlot(srt_sel,reduction = "spatial",group.by = "MHB")+coord_fixed()

sel_gene = rownames(markers.MHB[markers.MHB$p_val_adj<0.05,])
FeaturePlot(srt_sel,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,pt.size=0.3,ncol=4)

ggsave(file.path(fig_dir,paste0("markergene_23_27_MHB.pdf")))
Saving 10 x 8 in image

eye genes

eye_genes = c("Rax", "Vax1", "Vax2", "Pax2", "Six6")
FeaturePlot(srt33,features = eye_genes,reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)

srt33_eye = ScaleData(srt33,features = unique(c(VariableFeatures(srt33), eye_genes)) ,verbose=F)
expand_genes = c()
for(ge in c("Rax","Vax1","Six6")){
  print(ge)
  corr = t(cor((srt33_eye@assays$RNA@scale.data[ge,]), t(srt33_eye@assays$RNA@scale.data )))[,1]
  corr = corr[!is.na(corr)]
  corr = corr[order(corr,decreasing = T)]
  expand_genes = c(expand_genes, names(head(corr))[2:6])
}
[1] "Rax"
Warning in cor((srt33_eye@assays$RNA@scale.data[ge, ]), t(srt33_eye@assays$RNA@scale.data)) :
  the standard deviation is zero
[1] "Vax1"
Warning in cor((srt33_eye@assays$RNA@scale.data[ge, ]), t(srt33_eye@assays$RNA@scale.data)) :
  the standard deviation is zero
[1] "Six6"
Warning in cor((srt33_eye@assays$RNA@scale.data[ge, ]), t(srt33_eye@assays$RNA@scale.data)) :
  the standard deviation is zero
srt33_eye = FindNeighbors(srt33_eye,dims = 1:5,verbose=F)

sel_spa = srt33_eye@reductions$spatial@cell.embeddings[srt33_eye$seurat_clusters==1,]
plot(sel_spa)

db_out = dbscan(sel_spa,eps=50)

ggplot(data=NULL,aes(x=sel_spa[,1],y=sel_spa[,2],col=as.factor(db_out$cluster)))+
  geom_point()

idx = rownames(sel_spa)[db_out$cluster==1]
srt33_eye$eye = "no"
srt33_eye$eye[colnames(srt33_eye) %in% idx] = "yes"
paste(sum(srt33_eye$eye=="yes"),"/",ncol(srt33_eye[,srt33_eye$RNA_snn_res.1 %in% c(0,1,15,21,23,27)]),"=",sum(srt33_eye$eye=="yes")/ncol(srt33_eye[,srt33_eye$RNA_snn_res.1 %in% c(0,1,15,21,23,27)]))
[1] "105 / 4824 = 0.0217661691542289"
paste(sum(srt33_eye$eye=="yes"),"/",sum(srt33_eye$RNA_snn_res.1==0),"=", sum(srt33_eye$eye=="yes")/sum(srt33_eye$RNA_snn_res.1==0))
[1] "105 / 1499 = 0.0700466977985324"
paste(sum(srt33_eye$RNA_snn_res.1==0),"/",ncol(srt33_eye),"=", sum(srt33_eye$RNA_snn_res.1==0)/ncol(srt33_eye))
[1] "1499 / 18260 = 0.082092004381161"
save.csv(marker.eye[marker.eye$p_val_adj<0.01 & marker.eye$avg_log2FC>0,],file="data/eyes_marker.csv")
Error in save.csv(marker.eye[marker.eye$p_val_adj < 0.01 & marker.eye$avg_log2FC >  : 
  could not find function "save.csv"

expand_genes_eye = c()
for(ge in c("Cp","Vwc2")){
  print(ge)
  corr = t(cor((srt33_eye_sel@assays$RNA@scale.data[ge,]), t(srt33_eye_sel@assays$RNA@scale.data )))[,1]
  corr = corr[!is.na(corr)]
  corr = corr[order(corr,decreasing = T)]
  expand_genes_eye = c(expand_genes_eye, names(head(corr,n=10))[2:10])
}
[1] "Cp"
[1] "Vwc2"

DimPlot(srt33_eye_sel,reduction = "spatial",pt.size = 4)+coord_fixed()
ggsave(file.path(fig_dir,"eye_subset_clustering.pdf"))
Saving 7.29 x 4.5 in image


DotPlot(srt33_eye_sel,features = unique(c(unique(top10$gene),"Cp","Vwc2")),cols="Spectral")+coord_flip()
ggsave(file.path(fig_dir,"eye_subset_markers_dotplot.pdf"))
Saving 7.29 x 4.5 in image

FeaturePlot(srt33_eye_sel,features = c("Vwc2","Jkamp","Cp","Tlk1","Pon2","Ict1"),coord.fixed = T,sort.cell = T,reduction = "spatial",ncol = 3)
Warning: The sort.cell parameter is being deprecated. Please use the order parameter instead for equivalent functionality.
ggsave(file.path(fig_dir,"eye_subset_markers_spatial.pdf"))
Saving 7.29 x 4.5 in image

pp = FeaturePlot(srt33_eye,features = rownames(marker.eye)[1:12],order = T,raster = F,ncol = 4,coord.fixed = T,reduction = "spatial",max.cutoff = "q99")
ggsave(file.path(fig_dir,paste0("spatial_eye_markers.pdf")),plot = pp,width = 14,height = 9)
srt36 = srt95[,srt95$orig.ident=="201104_36"]
pp = FeaturePlot(srt36,features = rownames(marker.eye)[1:12],order = T,raster = F,ncol = 4,coord.fixed = T,reduction = "spatial",max.cutoff = "q99")
ggsave(file.path(fig_dir,paste0("spatial_eye_markers_36.pdf")),plot = pp,width = 14,height = 9)
load("~/scRNAseq_reference_datasets/WT.Robj")

FeaturePlot(WT,features = c("Six6","Cp","Vwc2"),order=T,split.by = "stage")
Warning in FeaturePlot(WT, features = c("Six6", "Cp", "Vwc2"), order = T,  :
  All cells have the same value (0) of Six6.
Warning in FeaturePlot(WT, features = c("Six6", "Cp", "Vwc2"), order = T,  :
  All cells have the same value (0) of Cp.
Warning in FeaturePlot(WT, features = c("Six6", "Cp", "Vwc2"), order = T,  :
  All cells have the same value (0) of Vwc2.

WT$eye_cluster = WT@assays$RNA@data["Six6",]>0.7
table(WT$eye_cluster)

FALSE  TRUE 
88322   457 

sparse.cor2

table(WT$eye_cluster,WT$stage)
       
        WT_65 WT_70 WT_75 WT_80 WT_85
  FALSE  2280 13218 27417 23298 22109
  TRUE      0     0     5    46   406
table(WT$eye_cluster,WT$cluster)
       
           0    1   10   11   12   13   14   15   16   17   18   19    2   20   21   22   23   24   25   26   27   28   29    3   30   31   32   33   34   35   36   37   38   39    4   40
  FALSE 3860 4211 3638 2578 2753 2077  862 1907  956  787 2255 5096 2908 2386  887 3041  298 3708 1064 2857  290  530 2650 2322 1819 1594 1158 1297 1555 1972 1606 3137  338 2575 1081 1434
  TRUE     0   85  168    1    2    0    0    0    1    0    0    0    0    0    0    0    0  175    0    1    0    0    0    0    0    1    0    0    2   16    1    0    1    1    0    0
       
          41    5    6    7    8    9
  FALSE  482 3635 2509 1311 4808 2090
  TRUE     0    0    1    0    0    1
WT$eye_clu_fine = "no"
WT$eye_clu_fine[ WT$cluster==10]="clu10"
WT$eye_clu_fine[WT$eye_cluster & WT$cluster==10]="eye_10"
WT$eye_clu_fine[ WT$cluster==24]="clu24"
WT$eye_clu_fine[WT$eye_cluster & WT$cluster==24]="eye_24"
table(WT$eye_clu_fine)

 clu10  clu24 eye_10 eye_24     no 
  3638   3708    168    175  81090 
markers_24 = FindMarkers(WT,ident.1 = "24",group.by = "cluster",only.pos = T,min.diff.pct = 0.1,verbose = F)
eye.24_ref = FindMarkers(WT,ident.1 = "eye_24",ident.2 = "clu24",group.by = "eye_clu_fine",min.diff.pct = 0.1,verbose = F)
eye.24_ref = eye.24_ref[eye.24_ref$p_val_adj<0.01,]
eye.24_ref = eye.24_ref[eye.24_ref$pct.1>0.3 & eye.24_ref$pct.2<0.1,]
eye.24_ref

srt85 = readRDS(file = "data/srt_E85.Rds")
FeaturePlot(srt85,features = c("Six6","Aldh1a3","Foxg1","Cp","Vwc2"),split.by = "orig.ident",ncol=4,reduction = "spatial",coord.fixed = T,order=T)
Warning in FeaturePlot(srt85, features = c("Six6", "Aldh1a3", "Foxg1", "Cp",  :
  All cells have the same value (0) of Six6.
Warning in FeaturePlot(srt85, features = c("Six6", "Aldh1a3", "Foxg1", "Cp",  :
  All cells have the same value (0) of Six6.
Warning in FeaturePlot(srt85, features = c("Six6", "Aldh1a3", "Foxg1", "Cp",  :
  All cells have the same value (0) of Aldh1a3.

ggsave(file.path(fig_dir,"eye_markers_E85.pdf"))
Saving 46 x 10 in image
---
title: "E95 brain analysis"
output: html_notebook
---

```{r}
library(Seurat)
library(ggplot2)
library(pheatmap)
library(tidyverse)
fig_dir = file.path("figs","brain_plot")
dir.create(fig_dir)
```

```{r}
srt95 = readRDS(file = "data/srt_E95.Rds")
```

```{r}
srt33 = srt95[,srt95$orig.ident=="201104_33"]
```

```{r}
library(dbscan)


db_out = dbscan(srt33@reductions$spatial@cell.embeddings,60)

ggplot(data=NULL,aes(x=srt33@reductions$spatial@cell.embeddings[,1],y=srt33@reductions$spatial@cell.embeddings[,2],col= factor(db_out$cluster)))+
         geom_point(size=0.8)+
  coord_fixed()+
  theme_classic()

sel_clu = names(table(db_out$cluster)[order(table(db_out$cluster),decreasing = T)][1])
sel_clu
srt33 = srt33[,db_out$cluster==sel_clu]
```


```{r}
DimPlot(srt33,reduction = "spatial")+coord_fixed()
```


```{r}
saveRDS(srt33,file="data/srt33_clean.Rds")
```



```{r}
sel_cluster = 23
```


```{r}
DimPlot(srt33,reduction = "spatial",cells.highlight=colnames(srt33)[srt33$seurat_clusters==sel_cluster])+ggtitle(paste0("cluster ",sel_cluster))+coord_fixed()
```

```{r}
srt_sel = srt33[,srt33$seurat_clusters==sel_cluster]

db_out = dbscan(srt_sel@reductions$spatial@cell.embeddings,60)

ggplot(data=NULL,aes(x=srt_sel@reductions$spatial@cell.embeddings[,1],y=srt_sel@reductions$spatial@cell.embeddings[,2],col= factor(db_out$cluster)))+
         geom_point(size=0.8)+
  coord_fixed()+
  theme_classic()

sel_clu = names(table(db_out$cluster)[order(table(db_out$cluster),decreasing = T)][1])
sel_clu
srt_sel = srt_sel[,db_out$cluster==sel_clu]
```


```{r}
library(edgeR)
library(limma)

cnts = srt_sel@assays$RNA@counts
cnts = cnts[rowSums(cnts)>20,]
cnts = as.matrix(cnts)
allcounts = DGEList(counts=cnts)
allcounts = estimateDisp(allcounts, robust=TRUE)
  design_mat = model.matrix(~ srt_sel@reductions$spatial@cell.embeddings[,2])
  fit = glmQLFit(allcounts, design_mat)
  lrt = glmQLFTest(fit)
  top = topTags(lrt,n=Inf)
  top
```


```{r}
srt_markers = srt_sel@misc$markers
top10 <- srt_markers[srt_markers$p_val_adj<0.01,] #%>% group_by(cluster) %>% top_n(n = 30, wt = -p_val_adj) # %>%  top_n(n = 5, wt = avg_logFC)
top10$pct_diff = top10$pct.1-top10$pct.2
top10 = top10[top10$pct_diff>0.1,]
top10 = top10 %>% group_by(gene) %>% top_n(n=1,wt=avg_log2FC)

top10 = top10[top10$cluster %in% sel_cluster,]
```

```{r}
top_sel = top$table[rownames(top$table) %in% top10$gene,]
top_sel = top_sel[top_sel$FDR<0.05,]
write.csv(top_sel,file =file.path(fig_dir, paste0("DE_markersforcluster_",sel_cluster,".csv")))
top_sel
```



```{r}
sel_gene = c("Tcf7l2","Nr2f2","Wfdc1","Tal2","Boc","Zic4")
FeaturePlot(srt_sel,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,pt.size=0.5,ncol=3)
ggsave(file.path(fig_dir,paste0("markergene_",sel_cluster,"_region.pdf")))
```

```{r,fig.width=9,fig.height=4}
FeaturePlot(srt33,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)
ggsave(file.path(fig_dir,paste0("markergene_",sel_cluster,"_allpuck.pdf")))
```


```{r}
sel_clusters = c(23, 27)
srt_sel = srt33[,srt33$seurat_clusters %in% sel_clusters]

db_out = dbscan(srt_sel@reductions$spatial@cell.embeddings,60)

ggplot(data=NULL,aes(x=srt_sel@reductions$spatial@cell.embeddings[,1],y=srt_sel@reductions$spatial@cell.embeddings[,2],col= factor(db_out$cluster)))+
         geom_point(size=0.8)+
  coord_fixed()+
  theme_classic()

sel_clu = names(table(db_out$cluster)[order(table(db_out$cluster),decreasing = T)][1])

srt_sel = srt_sel[,db_out$cluster==sel_clu]


cnts = srt_sel@assays$RNA@counts
cnts = cnts[rowSums(cnts)>20,]
cnts = as.matrix(cnts)
allcounts = DGEList(counts=cnts)
allcounts = estimateDisp(allcounts, robust=TRUE)
  design_mat = model.matrix(~ srt_sel@reductions$spatial@cell.embeddings[,2])
  fit = glmQLFit(allcounts, design_mat)
  lrt = glmQLFTest(fit)
  top = topTags(lrt,n=Inf)


srt_markers = srt_sel@misc$markers
top10 <- srt_markers[srt_markers$p_val_adj<0.01,] #%>% group_by(cluster) %>% top_n(n = 30, wt = -p_val_adj) # %>%  top_n(n = 5, wt = avg_logFC)
top10$pct_diff = top10$pct.1-top10$pct.2
top10 = top10[top10$pct_diff>0.2,]
#top10 = top10 %>% group_by(gene) %>% top_n(n=1,wt=avg_log2FC)

top10 = top10[top10$cluster %in% sel_clusters,]

top_sel = top$table[rownames(top$table) %in% top10$gene,]
top_sel = top_sel[top_sel$FDR<0.05,]
write.csv(top_sel,file =file.path(fig_dir, paste0("DE_markersforcluster_23_27.csv")))
top_sel
```

```{r}
sel_gene = c("Tcf7l2","Dmrta2","Rprm","Tubb3","Pcdh8","Shh")
FeaturePlot(srt_sel,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,pt.size=0.5,ncol=3)
ggsave(file.path(fig_dir,paste0("markergene_23_27_region.pdf")))
```

```{r,fig.width=9,fig.height=4}
FeaturePlot(srt33,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)
ggsave(file.path(fig_dir,paste0("markergene_23_27_allpuck.pdf")))
```

```{r}
DimPlot(srt_sel,reduction = "spatial")+coord_fixed()
```


```{r}
srt_sel$MHB = "no"
srt_sel$MHB[srt_sel$ycoord>2900 & srt_sel$ycoord < 3180 & srt_sel$xcoord>4900] = "yes"

DimPlot(srt_sel,reduction = "spatial",group.by = "MHB")+coord_fixed()
```


```{r}
markers.MHB = FindMarkers(srt_sel, group.by = "MHB",ident.1 = "yes",ident.2 = "no",verbose = F)
markers.MHB[markers.MHB$p_val_adj<0.05,]
```
```{r,fig.width=10,fig.height=8}
sel_gene = rownames(markers.MHB[markers.MHB$p_val_adj<0.05,])
FeaturePlot(srt_sel,features = sel_gene,reduction = "spatial",coord.fixed = T,order=T,pt.size=0.3,ncol=4)
ggsave(file.path(fig_dir,paste0("markergene_23_27_MHB.pdf")))
```

## eye genes

```{r}
eye_genes = c("Rax", "Vax1", "Vax2", "Pax2", "Six6")
FeaturePlot(srt33,features = eye_genes,reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)
```

```{r}
srt33_eye = ScaleData(srt33,features = unique(c(VariableFeatures(srt33), eye_genes)) ,verbose=F)
```



```{r}
expand_genes = c()
for(ge in c("Rax","Vax1","Six6")){
  print(ge)
  corr = t(cor((srt33_eye@assays$RNA@scale.data[ge,]), t(srt33_eye@assays$RNA@scale.data )))[,1]
  corr = corr[!is.na(corr)]
  corr = corr[order(corr,decreasing = T)]
  expand_genes = c(expand_genes, names(head(corr))[2:6])
}

```



```{r}
srt33_eye = RunPCA(srt33_eye, npcs = 5,features=unique(c(expand_genes,eye_genes)),verbose=F)
srt33_eye = FindNeighbors(srt33_eye,dims = 1:5,verbose=F)
srt33_eye = FindClusters(srt33_eye,verbose=F)
```


```{r}
DimPlot(srt33_eye,reduction = "spatial")
```


```{r}
sel_spa = srt33_eye@reductions$spatial@cell.embeddings[srt33_eye$seurat_clusters==1,]
plot(sel_spa)
db_out = dbscan(sel_spa,eps=50)

ggplot(data=NULL,aes(x=sel_spa[,1],y=sel_spa[,2],col=as.factor(db_out$cluster)))+
  geom_point()
idx = rownames(sel_spa)[db_out$cluster==1]
srt33_eye$eye = "no"
srt33_eye$eye[colnames(srt33_eye) %in% idx] = "yes"
```

```{r}
paste(sum(srt33_eye$eye=="yes"),"/",ncol(srt33_eye[,srt33_eye$RNA_snn_res.1 %in% c(0,1,15,21,23,27)]),"=",sum(srt33_eye$eye=="yes")/ncol(srt33_eye[,srt33_eye$RNA_snn_res.1 %in% c(0,1,15,21,23,27)]))

paste(sum(srt33_eye$eye=="yes"),"/",sum(srt33_eye$RNA_snn_res.1==0),"=", sum(srt33_eye$eye=="yes")/sum(srt33_eye$RNA_snn_res.1==0))

paste(sum(srt33_eye$RNA_snn_res.1==0),"/",ncol(srt33_eye),"=", sum(srt33_eye$RNA_snn_res.1==0)/ncol(srt33_eye))
```




```{r}
marker.eye = FindMarkers(srt33_eye,group.by = "eye", ident.1 = "yes",ident.2 = "no",min.diff.pct = 0.1,verbose = F)
write.csv(marker.eye[marker.eye$p_val_adj<0.01 & marker.eye$avg_log2FC>0,],file="data/eyes_marker.csv")
```


```{r}
DimPlot(srt33_eye,reduction = "spatial",group.by = "eye",cols = c("grey80","dodgerblue2"),pt.size=0.3)+coord_fixed()+theme_void()
ggsave(file.path(fig_dir,paste0("spatial_eye_region.pdf")))
```

```{r}
srt33_eye_sel = srt33_eye[,srt33_eye$eye=="yes"]
srt33_eye_sel = FindVariableFeatures(srt33_eye_sel,nfeatures = 1000)
srt33_eye_sel = ScaleData(srt33_eye_sel,features =  unique(c(VariableFeatures(srt33_eye_sel), c("Cp","Vwc2"))))
expand_genes_eye = c()
for(ge in c("Cp","Vwc2")){
  print(ge)
  corr = t(cor((srt33_eye_sel@assays$RNA@scale.data[ge,]), t(srt33_eye_sel@assays$RNA@scale.data )))[,1]
  corr = corr[!is.na(corr)]
  corr = corr[order(corr,decreasing = T)]
  expand_genes_eye = c(expand_genes_eye, names(head(corr,n=10))[2:10])
}
```


```{r}

srt33_eye_sel = RunPCA(srt33_eye_sel, npcs = 5,features=unique(c(expand_genes_eye,c("Cp","Vwc2"))),verbose=F)
srt33_eye_sel = FindNeighbors(srt33_eye_sel,k.param=10,dims = 1:5)
srt33_eye_sel = FindClusters(srt33_eye_sel)

DimPlot(srt33_eye_sel,reduction = "spatial",pt.size = 4)+coord_fixed()
ggsave(file.path(fig_dir,"eye_subset_clustering.pdf"))
```

```{r}
eye_sub.markers = FindAllMarkers(srt33_eye_sel)
top10 <- eye_sub.markers %>% group_by(cluster) %>% top_n(n = 10, wt = -p_val_adj) # %>%  top_n(n = 5, wt = avg_logFC)
top10 = top10 %>% group_by(gene) %>% top_n(n=1,wt=avg_log2FC)
top10 = top10 %>% group_by(cluster) %>% top_n(n = 5, wt = avg_log2FC)

DotPlot(srt33_eye_sel,features = unique(c(unique(top10$gene),"Cp","Vwc2")),cols="Spectral")+coord_flip()
ggsave(file.path(fig_dir,"eye_subset_markers_dotplot.pdf"))
```


```{r}
FeaturePlot(srt33_eye_sel,features = c("Vwc2","Jkamp","Cp","Tlk1","Pon2","Ict1"),coord.fixed = T,sort.cell = T,reduction = "spatial",ncol = 3)
ggsave(file.path(fig_dir,"eye_subset_markers_spatial.pdf"))
```



```{r, fig.width=12,fig.height=8}
pp = FeaturePlot(srt33_eye,features = rownames(marker.eye)[1:12],order = T,raster = F,ncol = 4,coord.fixed = T,reduction = "spatial",max.cutoff = "q99")
ggsave(file.path(fig_dir,paste0("spatial_eye_markers.pdf")),plot = pp,width = 14,height = 9)
```


```{r}
srt36 = srt95[,srt95$orig.ident=="201104_36"]
```

```{r, fig.width=12,fig.height=8}
pp = FeaturePlot(srt36,features = rownames(marker.eye)[1:12],order = T,raster = F,ncol = 4,coord.fixed = T,reduction = "spatial",max.cutoff = "q99")
ggsave(file.path(fig_dir,paste0("spatial_eye_markers_36.pdf")),plot = pp,width = 14,height = 9)
```

```{r}
load("~/scRNAseq_reference_datasets/WT.Robj")
```


```{r}
DimPlot(WT)
```


```{r}
FeaturePlot(WT,features = c("Six6","Cp","Vwc2"),order=T)
```

```{r,fig.width=14,fig.height=9}
FeaturePlot(WT,features = c("Six6","Cp","Vwc2"),order=T,split.by = "stage")
```

```{r}
hist(WT@assays$RNA@data["Six6",][WT@assays$RNA@data["Six6",]>0.5])
```


```{r}
WT$eye_cluster = WT@assays$RNA@data["Six6",]>0.7
table(WT$eye_cluster)
```
sparse.cor2

```{r}
DimPlot(WT,group.by="eye_cluster",order=T)
```

```{r}
table(WT$eye_cluster,WT$stage)
```

```{r}
table(WT$eye_cluster,WT$cluster)
```


```{r}
WT$eye_clu_fine = "no"
WT$eye_clu_fine[ WT$cluster==10]="clu10"
WT$eye_clu_fine[WT$eye_cluster & WT$cluster==10]="eye_10"
WT$eye_clu_fine[ WT$cluster==24]="clu24"
WT$eye_clu_fine[WT$eye_cluster & WT$cluster==24]="eye_24"
table(WT$eye_clu_fine)
```

```{r}
markers_24 = FindMarkers(WT,ident.1 = "24",group.by = "cluster",only.pos = T,min.diff.pct = 0.1,verbose = F)
```


```{r}
eye.10_ref = FindMarkers(WT,ident.1 = "eye_10",ident.2 = "clu10",group.by = "eye_clu_fine",min.diff.pct = 0.1,verbose = F)
eye.10_ref = eye.10_ref[eye.10_ref$p_val_adj<0.01,]
eye.10_ref = eye.10_ref[eye.10_ref$pct.1>0.3 & eye.10_ref$pct.2<0.1,]
eye.10_ref
```

```{r}
eye.24_ref = FindMarkers(WT,ident.1 = "eye_24",ident.2 = "clu24",group.by = "eye_clu_fine",min.diff.pct = 0.1,verbose = F)
eye.24_ref = eye.24_ref[eye.24_ref$p_val_adj<0.01,]
eye.24_ref = eye.24_ref[eye.24_ref$pct.1>0.3 & eye.24_ref$pct.2<0.1,]
eye.24_ref
```

```{r,fig.width=8,fig.height=6}
FeaturePlot(WT,features = rownames(eye.10_ref),order=T,ncol=3)
```
```{r,fig.width=8,fig.height=6}
FeaturePlot(WT,features = rownames(eye.24_ref),order=T,ncol=3)
```

```{r}
FeaturePlot(srt33,features = rownames(eye.10_ref),reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)
```

```{r}
FeaturePlot(srt33,features = rownames(eye.24_ref),reduction = "spatial",coord.fixed = T,order=T,ncol=3,pt.size=0.01)
```


```{r,fig.width=12,fig.height=6}
FeaturePlot(srt33,features = c("Six6","Aldh1a3"),reduction = "spatial",coord.fixed = T,order=T,blend = T,pt.size=0.2)
```

```{r}
srt85 = readRDS(file = "data/srt_E85.Rds")
```


```{r,fig.width=46,fig.height=10}
FeaturePlot(srt85,features = c("Six6","Aldh1a3","Foxg1","Cp","Vwc2"),split.by = "orig.ident",ncol=4,reduction = "spatial",coord.fixed = T,order=T)
ggsave(file.path(fig_dir,"eye_markers_E85.pdf"))
```



